✅ Complete Roadmap to Learn Power BI in 2025-26 📊🚀

Phase 1: Power BI Basics (1-2 Weeks)
🔹 Understand Power BI Desktop interface
🔹 Learn to connect to different data sources (Excel, CSV, databases)
🔹 Import & transform data using Power Query Editor

Phase 2: Data Modeling (1-2 Weeks)
🔹 Create relationships between tables
🔹 Understand star and snowflake schemas
🔹 Learn DAX basics — calculated columns, measures, basic functions

Phase 3: Data Visualization (2-3 Weeks)
🔹 Build reports using charts, tables, maps, slicers
🔹 Customize visuals and format reports
🔹 Use bookmarks and drill-through features

Phase 4: Advanced DAX & Analytics (2-3 Weeks)
🔹 Master advanced DAX functions (time intelligence, filter functions)
🔹 Create dynamic reports with variables and context
🔹 Use What-If parameters and forecasting

Phase 5: Power BI Service & Sharing (1-2 Weeks)
🔹 Publish reports to Power BI Service
🔹 Set up dashboards and workspaces
🔹 Share reports and collaborate with teams
🔹 Schedule data refresh and manage permissions

Phase 6: Integration & Automation (Optional)
🔹 Integrate Power BI with Excel, Teams, and SharePoint
🔹 Automate workflows using Power Automate

Phase 7: Real-World Projects & Certification
🔹 Build dashboards from real datasets
🔹 Prepare for Microsoft Power BI certification (DA-100 / PL-300)

💬 Tap ❤️ for the detailed explanation!

If I need to teach someone data analytics from the basics, here is my strategy:

1. I will first remove the fear of tools from that person

2. i will start with the excel because it looks familiar and easy to use

3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things

4. I will release the person from the tutorial hell and move into a more action oriented person

5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily

6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance

7. It helps the person to develop the analytical thinking

8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life

9. Then I move the person to power bi to do again 5 projects by using either sql or excel files

10. Now the fear is removed.

11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills

12. Further it helps you to clear case study round given by most of the companies

13. Now i help the person how to present them in resume and also how these tools are used in real world.

14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.

15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.

16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!

Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!

On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.

On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.

On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!

Ride the wave with AI into the future!

Tune in to the AI Journey webcast on November 19-21.

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.

5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
- Create calculated fields to derive new data:
- Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
- Use parameters to allow user input and control measures dynamically.

8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
- Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
- Export to PDF or image for offline use.

13. Keyboard Shortcuts
- Show/Hide Sidebar: Ctrl+Alt+T
- Duplicate Sheet: Ctrl + D
- Undo: Ctrl + Z
- Redo: Ctrl + Y

14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

✅ Top Data Analyst Interview Q&A 🎯

1. How do you handle messy or incomplete data in a real project
Answer:
I start by profiling the dataset to identify missing values, duplicates, and inconsistent formats. Depending on the context, I may impute missing values using mean/median, flag them for review, or exclude them if they’re not critical. For example, in an HR dataset, I used pandas to standardize date formats and fill missing department fields based on role noscripts.

2. Describe a time you built a dashboard that influenced a business decision
Answer:
At my previous role, I built a Power BI dashboard to track churn across customer segments. It revealed that users from a specific region had a 30% higher churn rate. This insight led the marketing team to launch a targeted retention campaign, reducing churn by 12% in the next quarter.

3. How do you approach a vague business question like “Why are sales dropping”
Answer:
I break it down by segmenting data—region, product, time period—and look for anomalies or trends. I compare current vs. previous periods, analyze customer behavior, and check for external factors. In one case, I discovered that a drop in sales was due to a discontinued product line that hadn’t been flagged in reporting.

4. What’s your process for analyzing an A/B test
Answer:
I define the hypothesis, ensure randomization, and check sample sizes. Then I compare metrics like conversion rate between control and test groups using statistical tests (e.g., t-test or chi-square). I also calculate p-values and confidence intervals to determine significance. I once helped a product team validate a new checkout flow that increased conversions by 8%.

5. How do you ensure your analysis is understandable to non-technical stakeholders
Answer:
I focus on clarity—use simple language, clean visuals, and highlight key takeaways. I avoid jargon and always tie insights to business impact. For example, instead of saying “standard deviation,” I might say “variation in customer spending.”

6. What tools do you use for forecasting and how do you validate your predictions
Answer:
I use Excel for quick models and Python’s statsmodels or Prophet for more robust forecasting. I validate predictions using historical data and metrics like RMSE or MAPE. In a recent project, I forecasted monthly sales and helped the inventory team reduce overstock by 15%.

7. How do you automate repetitive reporting tasks
Answer:
I use Python noscripts with scheduled jobs or Power BI’s refresh features. In one case, I automated a weekly sales report using Google Sheets + Apps Script, saving 5 hours of manual work per week.

8. How do you prioritize multiple data requests from different teams
Answer:
I assess urgency, business impact, and effort required. I communicate clearly with stakeholders and use frameworks like ICE (Impact, Confidence, Effort) to align priorities. I also maintain a request tracker to manage expectations.

Double Tap ♥️ For More

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!

Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?

On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.

On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of trannoscriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation ennoscriptd “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.

And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.

The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.

Ride the wave with AI into the future!

Tune in to the AI Journey webcast on November 19-21.

Free Resources to learn Power BI
👇👇

Microsoft Certified: Power BI Data Analyst Associate

https://learn.microsoft.com/en-us/certifications/power-bi-data-analyst-associate/?azure-portal=true

Visualize data in Power BI

https://learn.microsoft.com/en-us/training/paths/visualize-data-power-bi/?ns-enrollment-type=Collection&ns-enrollment-id=djwu3eywpk4name

Data analysis in Power BI

https://learn.microsoft.com/en-us/training/paths/perform-analytics-power-bi/?ns-enrollment-type=Collection&ns-enrollment-id=djwu3eywpk4nm

Learn Power BI Basics for Free

https://bit.ly/3sTa6Gj

Power BI Free Book

https://download.microsoft.com/download/0/8/1/0816F8D1-D1A5-4F60-9AF5-BC91E18D6D64/Microsoft_Press_ebook_Introducing_Power_BI_PDF_mobile.pdf

Power BI Dashboard Step by Step FREE UDEMY COURSE

https://bit.ly/3dho1Sleep

ENJOY LEARNING 👍👍

❌ Power BI alone won’t make you Data Analyst
❌ Power BI cannot get you a 18 LPA job offer
❌ Power BI cannot be mastered in 2 days
❌ Power BI is not just colorful dashboard
❌ Power BI is not simple “drag and drop”
❌ Power BI isn’t for Data Analysts only

But here’s what Power BI can do:

✔️ Power BI can save your reporting time
✔️ Power BI keeps your confidential data safe
✔️ Power BI helps you say bye to Pivot Tables
✔️ Power BI makes your report easy to consume
✔️ Power BI can update your dashboard with a single click
✔️ Power BI handles heavy data without testing your patience
✔️ Power BI is the next level for people whose work depends on Excel


I can go on and on, but you get the point.

Wrong expectations -> Wrong results
Right expectations -> Amazing results

✅ Power BI Roadmap for Beginners (2025) 📊🧠

1. Understand What Power BI Is
⦁ Business intelligence tool for data visualization and sharing insights
⦁ Types: Power BI Desktop (free), Service (cloud), Mobile app

2. Learn the Interface Basics
⦁ Views: Report, Data, Model
⦁ Navigation: Ribbons, fields pane, visualizations

3. Connect & Import Data
⦁ Sources: Excel, CSV, SQL databases, web
⦁ Use Power Query for initial cleaning and transformation

4. Learn Data Modeling
⦁ Relationships between tables
⦁ Star schema basics, hierarchies

5. Master Visualizations
⦁ Charts: Bar, line, pie, maps
⦁ Slicers, filters, drill-through

6. Practice with DAX Formulas
⦁ Basics: SUM, AVERAGE, CALCULATE
⦁ Measures and calculated columns

7. Build Interactive Reports
⦁ Dashboards, bookmarks, tooltips
⦁ Conditional formatting, themes

8. Work on Projects
⦁ Sales dashboard
⦁ KPI tracker
⦁ Customer analytics report

9. Learn Publishing & Sharing
⦁ Upload to Power BI Service
⦁ Workspaces, apps, scheduled refresh

10. Bonus Skills
⦁ Advanced DAX (time intelligence)
⦁ Power BI Copilot AI features
⦁ Integration with Excel/PowerPoint

💬 Double Tap ♥️ For More

✅ Power BI: Data Modeling 📊🧠

Data modeling is key to organizing tables and relationships for accurate insights in Power BI. Here’s a quick guide to the essentials and best practices for 2025:

🔹 1. What Is Data Modeling?
Organizing tables and defining relationships so Power BI understands your data structure.

🔹 2. Relationship Types
⦁ One-to-Many (most common)
⦁ Many-to-One
⦁ One-to-One
⦁ Many-to-Many (needs special care)

🔹 3. Keys in Relationships
⦁ Primary Key – unique in one table
⦁ Foreign Key – matching ID in another

🔹 4. Cardinality & Cross Filter Direction
Defines type and filtering behavior of relationships. Single direction is safer; bi-directional can be costly and tricky.

🔹 5. Star Schema (Best Practice)
Central fact table linked to dimension tables like Date, Product, Region—faster, simpler, and easier for calculations.

🔹 6. Snowflake Schema (Alternative)
Dimension tables have sub-dimensions. More normalized but complex and slower.

🔹 7. Model View Features
Drag to link tables, rename relationships, set properties, and mark proper date tables.

🔹 8. Common Modeling Mistakes
⦁ Circular relationships cause loops
⦁ Missing relationships break visuals
⦁ Wrong cardinality leads to bad aggregations
⦁ No date table causes time intelligence to fail

🔹 9. Tips for Better Models
⦁ Always use a proper Date Table
⦁ Clear, business-friendly names for tables and fields
⦁ Hide technical columns not needed in reports
⦁ Use DAX measures over calculated columns for performance

🔹 10. Test Your Model
Create visuals, apply filters to check relationships, use Matrix/Table view to inspect values.

💬 Double Tap ♥️ For More

✅ Power BI DAX Basics 📊🧠

DAX (Data Analysis Expressions) is Power BI's formula language for custom calculations, measures, and columns—essential for dynamic insights in 2025 reports.

1️⃣ SUM()
Adds column values.
Example: Total Sales = SUM(Sales[Amount])

2️⃣ AVERAGE()
Gets column average.
Example: Avg Salary = AVERAGE(Employee[Salary])

3️⃣ COUNT(), COUNTA(), COUNTROWS()
⦁ COUNT() for numerics
⦁ COUNTA() for non-blanks
⦁ COUNTROWS() for table rows
Examples: Total Orders = COUNT(Orders[OrderID]) | Total Customers = COUNTROWS(Customers)

4️⃣ CALCULATE()
Modifies calculation context (DAX's powerhouse).
Example: Sales East = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")

5️⃣ FILTER()
Builds filtered tables for CALCULATE.
Example: High Sales = CALCULATE(SUM(Sales[Amount]), FILTER(Sales, Sales[Amount] > 1000))

6️⃣ IF()
Adds conditional logic.
Example: Sales Category = IF(Sales[Amount] > 500, "High", "Low")

7️⃣ SWITCH()
Handles multiple conditions cleanly.
Example: Rating = SWITCH(TRUE(), [Score] >= 90, "A", [Score] >= 75, "B", [Score] >= 60, "C", "Fail")

8️⃣ ALL()
Removes filters for totals.
Example: % of Total = DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALL(Sales)))

9️⃣ DISTINCT()
Gets unique column values.
Example: Unique Products = DISTINCT(Sales[Product])

🔟 Measures vs Calculated Columns
⦁ Measures: Dynamic, context-based (best for visuals)
⦁ Calculated Columns: Row-by-row, stored (use sparingly for performance)

💬 Tap ❤️ for more!

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* CALCULATE() – the most powerful DAX function; modifies filter context to compute custom calculations

* FILTER() – creates a row context filter, often used inside CALCULATE

* ALL() – removes filters; helps in calculating things like overall totals or percent of total

* RELATED() – pulls data from a related table (like a VLOOKUP)

* SUMX() – performs row-wise iteration with custom logic and sums the result

* RANKX() – assigns rank to rows based on a measure/column, useful for top-N reports

* SWITCH() – an alternative to nested IFs; cleaner for handling multiple conditions

React ❤️ for more

Many people pay too much to learn Excel, but my mission is to break down barriers. I have shared complete learning series to learn Excel from scratch.

Here are the links to the Excel series

Complete Excel Topics for Data Analyst: https://news.1rj.ru/str/sqlspecialist/547

Part-1: https://news.1rj.ru/str/sqlspecialist/617

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Part-12:
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Part-13: https://news.1rj.ru/str/sqlspecialist/650

Part-14: https://news.1rj.ru/str/sqlspecialist/651

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Part-19: https://news.1rj.ru/str/sqlspecialist/661

Part-20: https://news.1rj.ru/str/sqlspecialist/662

Bonus: https://news.1rj.ru/str/sqlspecialist/663

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

You can join this telegram channel for more Excel Resources: https://news.1rj.ru/str/excel_analyst

Python Learning Series: https://news.1rj.ru/str/sqlspecialist/615

Complete SQL Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/523

Complete Power BI Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/588

I'll now start with learning series on SQL Interviews & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)